research questions about high school dropouts

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High school dropouts: Interactions between social context, self-perceptions, school engagement, and student dropout ☆

Research suggests that contextual, self-system, and school engagement variables influence dropping out from school. However, it is not clear how different types of contextual and self-system variables interact to affect students’ engagement or contribute to decisions to dropout from high school. The self-system model of motivational development represents a promising theory for understanding this complex phenomenon. The self-system model acknowledges the interactive and iterative roles of social context, self-perceptions, school engagement, and academic achievement as antecedents to the decision to dropout of school. We analyzed data from the Education Longitudinal Study of 2002–2004 in the context of the self-system model, finding that perception of social context (teacher support and parent support) predicts students’ self-perceptions (perception of control and identification with school), which in turn predict students’ academic and behavioral engagement, and academic achievement. Further, students’ academic and behavioral engagement and achievement in 10th grade were associated with decreased likelihood of dropping out of school in 12th grade.

Almost one-third of all public secondary students in the United States each year dropout of school ( Snyder & Dillow, 2010 ; Stillwell, 2010 ). Dropout rates vary across groups and settings, with Hispanic (36.5%) and African American (38.5%) students dropping out at higher rates than Asian (8.6%) and White (19%) students ( Stillwell, 2010 ). High rates of dropout affect individuals, families, and communities ( Dynarski, Gleason, Rangarajan, & Wood, 1998 ; Orfield, 2006 ). Nongraduates are more likely to be unemployed ( Sum, Khatiwada, McLaughlin, & Palma, 2009 ), to earn less when employed ( Levin, Belfield, Muennig, & Rouse, 2007 ), to receive public assistance ( Waldfogel, Garfinkel, & Kelly, 2007 ), to suffer poor health ( Muennig, 2007 ), and to have higher rates of criminal behavior and incarceration ( Moretti, 2007 ). Additionally, children of parents who did not complete high school are more likely to perform poorly in school and eventually dropout, creating an intergenerational dynamic ( Orfield, 2006 ).

Considerable research has addressed factors associated with dropping out of school. Early attempts to identify risk focused on student factors associated with an elevated likelihood of leaving school prior to graduating. This research consistently reports that students from poor or single-parent households, or whose parents did not graduate from high school, are at greater risk of dropping out from school than students from families without these risk factors ( Alexander, Entwisle, & Horsey, 1997 ; Goldschmidt & Wang, 1999 ; Rumberger, 1995 ; Rumberger & Larson, 1998 ; Swanson & Schneider, 1999 ). The earlier research also suggests that students with adult responsibilities ( Cairns, Cairns, & Neckerman, 1989 ; Gleason & Dynarski, 2002 ; Goldschmidt & Wang, 1999 ; Neild & Balfanz, 2006 ), with a sibling who has dropped out ( Teachman, Paasch, & Carver, 1996 ), who have been retained ( Goldschmidt & Wang, 1999 ; Roderick, 1994 ; Roderick, Nagaoka, Bacon, & Easton, 2000 ; Rumberger, 1995 ; Rumberger & Larson, 1998 ), or who have changed schools ( Astone & McLanahan, 1994 ; Rumberger, 1995 ; Rumberger & Larson, 1998 ; Swanson & Schneider, 1999 ) are more likely to dropout of school.

Although this early work centered on person-level characteristics that tend not to be amenable to change, more recent research addresses dynamic factors related to risk status and has led to a growing interest in the construct of engagement ( Appleton, Christenson, Kim, & Reschly, 2006 ; Fredricks, Blumenfeld, & Paris, 2004 ; Sinclair, Christenson, Lehr, & Anderson, 2003 ). School engagement is considered the primary model for understanding and predicting graduation from high school. Conceptualizations of school engagement vary in their details ( Appleton, Christenson, & Furlong, 2008 ; Finn, 1989 ; Fredricks et al., 2004 ; Jimerson, Campos, & Greif, 2003 ). However, they share a premise: that poor school engagement hinders academic achievement ( Caraway, Tucker, Reinke, & Hall, 2003 ; DiPerna, Volpe, & Elliott, 2005 ; Finn & Rock, 1997 ; Wu, Hughes, & Kwok, 2010 ), which, over time, increases the likelihood that students will dropout of school ( Alexander et al., 1997 ; Sinclair et al., 2003 ).

Theories of school dropout ( Appleton et al., 2008 ; Fredricks et al., 2004 ; Rumberger, 2006 ) and a growing body of research also suggests that contextual ( Dotterer & Lowe, 2011 ; Hong & Ho, 2005 ; Patrick, Ryan, & Kaplan, 2007 ; Ryan & Patrick, 2001 ; Wang & Holcombe, 2010 ; You & Sharkey, 2009 ) and self-system ( Caraway et al., 2003 ; Furrer & Skinner, 2003 ; You & Sharkey, 2009 ) variables influence school engagement and dropping out from school. However, it is not clear how aspects of social context influence multiple forms of engagement simultaneously or how different types of contextual and self-system variables interact to affect students’ engagement and lead to decisions to dropout from high school ( Fredricks et al., 2004 ). The self-system model of motivational development (SSMMD) integrates contextual and self-system variables and provides a framework for describing processes that initiate and sustain a decline in student engagement ( Connell & Wellborn, 1991 ; Skinner, Furrer, Marchand, & Kindermann, 2008 ; Skinner, Kindermann, Connell, & Wellborn, 2009 ; Skinner & Wellborn, 1994 ). Using the SSMMD, the central objective of the present study is to empirically test the mechanism involved in the dropout process.

Self-system model of motivational development

SSMMD posits that individuals possess an innate need to connect with others and interact effectively with their environment. It also asserts that the relationship of a given social context (e.g., family support, teacher support, peer support) and an individual’s self-system processes (e.g., perceived identification with school, perceived control) is influenced by the extent to which the social context meets or ignores (fulfills or neglects) these basic needs. Further, self-system profiles differentially influence engagement-related behaviors, which directly contribute to educational outcomes such as student achievement and dropping out. That is, SSMMD suggests that 1) self-systems mediate the relation between a social context and school engagement and that 2) engagement mediates the relation between self-system processes and student outcomes. This model is shown in Fig. 1 .

Self-system model of motivational development applied to dropping out of high school. Dotted lines represent significant indirect effects, and solid lines indicate significant direct effects. Adapted from Connell and Wellborn (1991) ; Skinner et al. (2008) ; and Skinner et al. (2009) .

Studies have provided empirical support for SSMMD, as applied to academic achievement ( Connell, Spencer, &Aber, 1994 ; Skinner et al., 2008 ; Wang & Holcombe, 2010 ). For instance, Connell et al. (1994) conducted path analyses among a sample of 10- to 16-year-old African American youth. Nearly all proposed relations based on SSMMD were significant. In particular, students’ perception of parental involvement predicted self-system processes (a composite measure of perceived competence, perceived relatedness to self, and perceived relatedness to others), which in turn predicted students’ emotional and behavioral engagement. Engagement predicted educational outcomes (a composite measure reflecting the degree of risk for school departure based on attendance, test scores, grade-point average, suspension, and retention). Skinner et al. (2008) found that teacher support and students’ self-system processes (perceived control, autonomy orientation, and sense of relatedness) were significant predictors of behavioral and emotional engagement. Moreover, self-system processes mediated the association between teacher support and student engagement. More recently, Wang and Holcombe (2010) examined the relationships among middle school students’ perception of school environment, engagement, and achievement. Structural equation modeling revealed that students’ perception of school environment in seventh grade (performance goal structure, mastery goal structure, support of autonomy, promotion of discussion, and teacher social support) affected their school engagement (behavioral, emotional, and cognitive engagement) and, in turn, influenced students’ academic achievement in eighth grade.

In contrast, empirical support for SSMMD, as applied to dropping out from high school, is limited. Available evidence, however, suggests that SSMMD can provide an organizing framework for better understanding the role of contextual, self-system, and engagement variables in dropping out from high school. To illustrate, Connell, Halpern-Felsher, Clifford, Cri-chlow, and Usinger (1995) used SSMMD to examine behavioral, psychological, and contextual predictors of staying in high school among a sample of African American adolescents. The authors found that higher levels of support from teachers and adults at home were associated with higher levels of perceived competence, perceived relatedness, and perceived autonomy. These self-perceptions then predicted students’ level of school engagement. School engagement positively predicted males’ staying in school. Among females, the association between engagement and staying in school was not significant. These findings, while promising, are subject to important limitations, notably the unidimensional conceptualization of the engagement construct and the relatively homogenous sample (African American adolescents from an urban school district). There is a need for replication with other populations. There may also be value in applying more nuanced conceptualizations of the engagement construct, including multidimensional models.

Researchers have tended to study the impact of either teacher or parent support on self-system and engagement ( Hong & Ho, 2005 ; Patrick et al., 2007 ; Ryan & Patrick, 2001 ; You & Sharkey, 2009 ). Past research has linked teacher support to student self-system and engagement. Support from teachers enhanced students’ focus on mastery of goals ( Patrick et al., 2007 ), feeling of academic efficacy ( Patrick et al., 2007 ; Ryan & Patrick, 2001 ), and self-regulated learning ( Ryan & Patrick, 2001 ), which in turn facilitated students’ cognitive and behavioral engagement ( Patrick et al., 2007 ; Ryan & Patrick, 2001 ). Although support from teachers is important for student learning and development, support from parents is also related to student self-perception and engagement. Parent support promotes students’ perception of control and perception of self, which in turn promote engagement and benefit student learning ( Hong & Ho, 2005 ; You & Sharkey, 2009 ). However, few studies have examined the impact of both supports in a single study. As a consequence, little has been learned about how parent and teacher support influence and differentially predict students’ self-system and engagement.

Finally, the relationship of engagement and dropping out is understood primarily in terms of student behavior. For example, Finn and Rock (1997) found that behavioral engagement significantly differentiated unsuccessful school completers, successful school completers, and school dropouts among 1803 minority students from low-income backgrounds. Rumberger (1995) , using data from the National Education Longitudinal Study of 1988, found that moderate to high absenteeism, behavior problems, and having no school or outside activities were highly predictive of dropping out. More recently, Ream and Rumberger (2008) investigated the effect of behavioral engagement on school completion and dropout among Mexican American and non-Latino White students, finding that engagement directly influenced high school graduation. Archambault, Janosz, Fallu, and Pagani (2009) used a three-part engagement construct encompassing behavioral, affective, and cognitive dimensions to successfully predict dropout. Although the global measures of engagement predicted school dropout, behavioral engagement was the only unique factor with statistically significant predictive value. In contrast, few studies have examined academic engagement as it relates to dropping out from high school.

Purposes of the present study

The present study addresses limitations in the research on engagement and dropping out where SSMMD provides the theoretical framework. We assess how indicators of social context (e.g., teacher and parent support), self-systems (e.g., perception of control, identification with school), and engagement (e.g., behavioral and academic engagement) relate to academic achievement and dropping out of high school. Fig. 1 depicts the proposed model, which comprises five parts. We hypothesized that higher levels of support from teachers and parents would positively influence students’ perception of self, that positive self-perceptions would positively influence students’ behavioral and academic engagement and academic achievement, and that high levels of behavioral and academic engagement and achievement would decrease the likelihood of dropping out of high school. We further anticipated that self-perceptions would mediate the relations between teacher and parent support and academic and behavioral engagement and that academic and behavioral engagement would mediate the relationship between the two self-perceptions and dropping out of high school.

Participants

Participants in this study were part of ELS: 2002–2004, designed by the National Center for Education Statistics to provide trend data about the experiences of a cohort of high school 10th-graders as they proceeded through high school and into postsecondary education or their careers ( Ingels, Pratt, Rogers, Siegel, & Stutts, 2004 ). The base-year study was carried out in a national probability sample of 752 public, Catholic, and private schools in the spring of the 2001–2002 academic year. In total, 15,362 students completed the base-year questionnaire, as did 13,488 parents, 14,081 teachers, 743 principals, and 718 librarians ( Ingels et al., 2007 ). The first-follow-up survey occurred in 2004, when most sample members were high school seniors—others had dropped out or completed high school early. The second follow-up occurred in 2006, when many sample members were in college for up to their second year of enrollment and others were employed. One additional follow-up is planned for 2012 to document later outcomes, including persistence in higher education or transition into the job market ( Ingels et al., 2007 ). For detailed information about ELS: 2002–2004, please see http://nces.ed.gov/surveys/els2002 .

We used the sample of 14,781 base-year students who participated in the first wave of the study and who were resurveyed in 2004 and identified as either still enrolled in school ( n = 13,995) or dropped out ( n = 786). Of this sample, 49.4% ( n = 7309) were male and 50.6% ( n = 7472) were female. Approximately 57% ( n = 8459) of the participants were White, 14.4% Hispanic ( n = 2126), 13.3% African American ( n = 1962), 9.5% Asian ( n = 1401), and 5.6% ( n = 833) American Indian or of mixed race. Table 1 describes in further detail the demographic characteristics of the sample by dropout status. To generate national population estimates for our analyses, we used the base-year/first-follow-up panel weight.

Demographic characteristics of the sample by dropout status.

Note. N = 14,781.

We drew all data for this study, except dropout status, from the base-year survey, when students were in 10th grade. For dropout status, we used data from the second wave, when most of the students were in 12th grade.

Parent support in 10th grade

Six items from the student questionnaire measured parent support, capturing the frequencies of parent and school communications concerning students’ school problems. On a 3-point scale (never, sometimes, and often), students reported the frequency with which they and their parents spoke about school in general, school-related activities, topics studied in class, and issues that troubled them. The following is a sample item: “In the first semester or term of this school year, how often have you discussed things you’ve studied in class with either or both of your parents or guardians?” Higher scores reflected greater parent support. The construct reliability ( Hancock & Mueller, 2001 ) of this latent variable was .83.

Teacher support in 10th grade

This latent construct represents students’ perceptions of the level of care and support from teachers. The construct included five items, and responses ranged from 1 (strongly agree) to 4 (strongly disagree). The following is a sample item: “In class, I often feel ‘put down’ by my teachers.” Items were coded, so that higher scores represented greater teacher support. The construct reliability was .74.

Perceived control in 10th grade

Perceived control included 4 items from the student questionnaire that assessed the extent to which students believed they were able to produce positive, and prevent negative, outcomes in school. Responses were rated on a 4-point scale (almost never, sometimes, often, and almost always). The following is a sample item: “When I sit myself down to learn something really hard, I can learn it.” Higher scores indicated higher perceived control. The construct reliability was .84.

Perceived identification with school in 10th grade

Identification with school included three items from the student questionnaire that measured students’ interest and satisfaction with school. Two items had response options on a 4-point scale, ranging from 1 (strongly agree) to 4 (strongly disagree). The following is a sample item: “I go to school because I think the subjects I’m taking are interesting and challenging.” The item “How much do you like school?” had response options ranging from 1 (not at all) to 3 (a great deal). Items were coded, so that higher scores indicated higher perceived identification with school. The construct reliability of this scale was .78.

School engagement in 10th grade

The school engagement index consisted of 12 items that measured behavioral and academic dimensions of engagement. The items were coded, so that higher scores reflected higher levels of school engagement. Behavioral engagement included four items from the student questionnaire that measured the extent to which students conformed to classroom norms, such as not skipping school and not getting in trouble. Responses were rated on a 5-point scale (never, 1–2 times, 3–6 times, 7–9 times, and 10 or more times). The following is a sample item: “I got in trouble for not following school rules.” The construct reliability of this latent variable was .69. The academic engagement scale included eight items from a questionnaire that measured English and mathematics teachers’ perception of student effort, persistence, and attention in their classes (for more details, see Table 2 ). Responses for four items were rated on a 2-point scale (yes or no). The following is a sample item: “Does this student usually work hard for good grades in your class?” Responses for another four items were rated on a 5-point scale (never, rarely, some of the time, most of the time, and all of the time). The following is a sample item: “How often does this student complete homework assignments for your class?” The construct reliability of this scale was .80.

Standardized parameter estimates from the confirmatory factor analysis model.

Academic achievement

Academic achievement was estimated as a latent variable using standardized T -scores in math and reading. The standardized T -score provided a norm-referenced measurement of achievement with a mean of 50 and standard deviation of 10.

The construct reliability of this scale was .86.

Student dropout status in 12th grade

To ascertain the impact of school engagement on dropout, we used the ELS: 2002–2004 12th-grade measure of dropout status (1 = enrolled in 12th grade, 0 = identified spring term 2004 dropouts). Dropouts were defined as 10th-grade cohort members who were not enrolled in school during the spring term 2 years later, who had not received a high school diploma or general educational development credentials, and who had missed 4 or more consecutive weeks not due to accident or illness.

Plan for analysis

To answer the research questions, our analysis comprised two steps. First, we assessed the fit of the measurement model, using confirmatory factor analyses (CFAs). Second, we used structural equation modeling to test our hypothesized model of dropping out of high school.

We used bootstrapping to test the indirect effects (see Preacher & Hayes, 2008 ; Shrout & Bolger, 2002 ). We requested the recommended minimum of 500 bootstrap samples ( Cheung & Lau, 2008 ) drawn with replacement from the full dataset of 14,781 cases. Bootstrapping is a recommended method for testing mediation, as it does not require the normality assumption and has greater statistical power and control for Type I error than the widely used three-step multiple regression approach ( Baron & Kenny, 1986 ) or the Sobel ( Sobel, 1982 ) test ( Fairchild & McQuillin, 2010 ; Lau & Cheung, in press ; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002 ; MacKinnon, Lockwood, & Williams, 2004 ; Preacher & Hayes, 2004 ; Shrout & Bolger, 2002 ). Support for a mediating role is indicated if the bootstrap (bias-corrected) confidence interval does not include zero. In that case, we can conclude that there is a 95% probability that the indirect or mediating effect is significant.

We conducted all statistical analyses with Mplus 5.21 ( Muthén & Muthén, 1993–2010 ). Because most of our measures are categorical, we used robust mean- and variance-adjusted weighted least squares (WLSMV) to estimate our models. The WLSMV estimator produces consistent parameter estimates, unbiased standard errors, and corrects χ 2 when there are categorical variables ( Brown, 2006 ; Muthén & Satorra, 1995 ). WLSMV utilizes all available data without either imputing values or deleting cases, based on the assumption that missing data is missing completely at random ( Little, 1995 ).

To evaluate the fits of the measurement and structural models, we relied on a set of test statistics: the Steiger–Lind root mean square error of approximation (RMSEA; Steiger, 1990 ), the Bentler comparative fit index (CFI; Bentler, 1990 ), and the Tucker–Lewis index (TLI), which are less sensitive to large samples than the more traditional chi-square statistic. We followed the Hu and Bentler (1999) guidelines for evaluating the fit between the target model and the observed data: 1) RMSEA values less than 0.05 indicate excellent fit, and values in the vicinity of 0.08 indicate acceptable fit; 2) CFI and TLI values of .95 or greater indicate an excellent fit, and coefficients of 0.90 indicate a good fit.

Measurement model

The first step in our analyses involved confirming the existence of our hypothesized latent constructs via CFAs. In the first CFA model, we specified a five-factor model to verify the structure of school engagement in terms of behavioral engagement and academic engagement, the structure of self-system processes in terms of perception of control and identification with school, and the structure of academic achievement. As Table 2 suggests, all item parcels loaded significantly onto their respective factors, with standardized loadings ranging from .48 to .67 on behavioral engagement, from .20 to .87 on academic engagement, from .69 to .83 on perception of control, and from .62 to .79 on identification with school. Each of the overall goodness-of-fit indices suggested that the five-factor model fit sample data very well, χ 2 (176) = 2078.808, p > .05, RMSEA = .027, RMSEA C.I. = .026–.028, CFI = .96, TLI = .96.

In the second CFA model, we specified a two-factor model to verify the structure of teacher support and parent support. All item parcels loaded significantly onto their respective factors, with standardized loadings ranging from .42 to .78 on teacher support, and from .55 to .77 on parent support. Each of the overall goodness-of-fit indices suggested that the two-factor model fit data well, χ 2 (43) = 521.202, p > .05, RMSEA = .027, RMSEA C.I. = .025–.030, CFI = .98, TLI = .97.

We next fitted a measurement-only model, which is equivalent to fitting a CFA while simultaneously allowing all factors to correlate with one another. The measurement model showed a good fit to the data, χ 2 (465) = 5541.743, p > .05, RMSEA = 0.027, CFI = 0.97, TLI = 0.96. Table 3 presents the correlations among the latent constructs in the model. All variables appeared to have low to moderate correlations (from −.01 to .61), allowing us to eliminate the problems of multicollinearity ( Kline, 2005 ). In conclusion, the results of CFA supported the measurement component of the proposed model, suggesting that items adequately measured their underlying latent factors.

Intercorrelation among latent and observed variables.

Structural model

We used structural equation modeling to examine how social context, self-perceptions, school engagement, and academic achievement contribute to dropping out of high school. According to the hypothesized model (see Fig. 1 ), student perceptions of teacher and parent support predict students’ perceptions of control and identification with school, which in turn predict students’ behavioral and academic engagement and academic achievement, which in turn predict dropout. The hypothesized model fit the observed data well (fit indices for the model without bootstrap resampling procedure: χ 2 (472) = 8297.830, p > .05, RMSEA = .033, CFI = .96, TLI = .95). SSMMD-related constructs, as a whole, accounted for 36.8% of the variance in dropping out of high school. Fig. 2 is a path diagram showing the fully standardized direct effects. Table 4 shows the specific mediation effects, the bootstrap estimates, and the 95% bias-corrected confidence intervals. For the sake of clarity, we first describe the direct paths within the model and then present the indirect effects.

Standardized coefficients for the self-system model of motivational development applied to dropping out of high school. Only significant direct paths ( p < .05) are shown.

Standardized bootstrap estimates and 95% bias-corrected confidence intervals for indirect effects.

Note. N = 14,781. BC 95% CI = bias-corrected 95% confidence intervals (if does not contain zero, the mediated effect is significant);

PC = perceived control; DO = dropping out from high school; BE = behavioral engagement; AE = academic engagement; IS = identification with school; PS = parent support; TS = teacher support.

Direct effects between social context and self-system processes

Both contextual variables were positively associated with students’ perception of control ( β = .26, p < .05 for teacher support; β = .34, p < .05 for parent support) and identification with school ( β = .51, p < .05 for teacher support; β = .23, p < .05 for parent support). That is, as students’ perception of teacher support and parent support increased, their positive perception of control and of identification with school also increased.

Direct effects between social context and school engagement

We also tested the direct paths from contextual variables to school engagement. The results indicated that both contextual variables significantly contributed to academic ( β = .18, p < .05 for teacher support; β = .16, p < .05 for parent support) and behavioral engagement ( β = .22, p < .05 for teacher support; β = .12, p < .05 for parent support).

Direct effects between self-system processes and school engagement and academic achievement

Perceived control was positively associated with academic engagement ( β = .19, p < .05) and academic achievement ( β = .39, p < .05). Identification with school was positively associated with behavioral ( β = .24, p < .05) and academic ( β = .04, p < .05) engagement and negatively associated with academic achievement ( β = −.25, p < .05). Perceived control was not a significant predictor of behavioral engagement ( β = .03, p > .05).

Direct effects between school engagement and academic achievement

Academic and behavioral engagement were positively associated with achievement ( β = .33, p < .05 and β = .11, p < .05 respectively).

Direct effects between school engagement and achievement in 10th grade and dropping out of school in 12th grade

Behavioral and academic engagement and achievement were associated with decreased likelihood of dropping out of high school ( β = −.30, p < .05, β = −.27, p < .05, and β = −.20, p < .05, respectively).

Mediated effects between self-system processes in 10th grade and dropping out of school in 12th grade

The specific indirect effect of perception of control on dropping out of high school through academic engagement was significant ( β = −.05, BC 95% CI = −.07, −.03) and through behavioral engagement was not significant ( β = −.01, BC 95% CI = −.02, .01). Greater perception of control led to greater academic engagement, which in turn decreased the probability of dropping out of high school. The direct effect of perceived control on dropping out of high school was not significant ( β = −.03, p > .05), implying that academic engagement fully mediated the relations between perceived control and dropping out of high school. Additionally, the specific indirect effect of identification with school on dropping out of high school through behavioral engagement was β = −.07, BC 95% CI = −.10, −.05 and through academic engagement was β = −.01, BC 95% CI = −.02, .00. Greater identification with school led to greater behavioral and academic engagement, which in turn decreased the probability of dropping out of high school. The direct effect of perceived identification with school on dropping out of high school was not significant ( β = .05, p > .05), indicating that behavioral and academic engagement fully mediated the relations between identification with school and dropping out of high school.

Mediated effects between social context and school engagement

Our first outcome variable of interest in these mediation analyses was academic engagement. The specific indirect effect of teacher support on academic engagement through perception of control was β = .05, BC 95% CI = .04, .06 and through identification with school was β = .02, BC 95% CI = .001, .04. That is, greater teacher support led to greater perceived control and identification with school, which in turn increased academic engagement. The specific indirect effect of parent support on academic engagement through perception of control was β = .06, BC 95% CI = .05, .08 and through identification with school was β = .01, BC 95% CI = .001, .02. In other words, greater parent support led to greater perceived control and of identification with school, which in turn increased academic engagement. The direct effect of teacher support on academic engagement was significant ( β = .18, p < .05) and so was the direct effect of parent support on academic engagement ( β = .16, p < .05). These findings indicate that self-system processes partially mediated the relations between the social context and academic engagement.

Our second outcome variable of interest was behavioral engagement. The specific indirect effect of teacher support on behavioral engagement through perception of control was not significant ( β = .01, BC 95% CI = −.01, .02) and through identification with school was significant ( β = .12, BC 95% CI = .10, .14). The specific indirect effect of parent support on behavioral engagement through perception of control was not significant ( β = .01, BC 95% CI =−.01, .02) and through identification with school was significant ( β = .05, BC 95% CI = .04, .07). The direct effect of teacher support on behavioral engagement was significant ( β = .22, p < .05) and so was the direct effect of parent support on behavioral engagement ( β = .12, p < .05). These data indicate that identification with school partially mediated the relation between contextual variables and behavioral engagement.

The purpose of this study was to examine the interdependence of school engagement and dropping out in the context of SSMMD. The self-system model proved to be valid. First, results revealed that contextual factors, including teacher support and parent support, positively influenced students’ self-perceptions (perceived control and identification with school) and school engagement (academic and behavioral). Second, students’ perceived control positively influenced academic engagement and achievement, while identification with school negatively influenced achievement and positively influenced academic and behavioral engagement. Third, as expected, academic and behavioral engagement positively influenced students’ achievement, and academic and behavioral engagement and achievement measured in 10th grade influenced dropping out of school in 12th grade. Fourth, engagement fully mediated the relation between the self-systems and dropping out of high school. Also, self-systems partially mediated the relation between the social context and school engagement. Given these results, the present study contributes to the school engagement and dropout literature in five ways.

First, using data from a nationally representative sample, this study provides empirical support for SSMMD as applied to the important problem of dropping out of high school. Although similar models of school dropout were recently proposed by Appleton et al. (2008) , Fredricks et al. (2004) , and Rumberger (2006) , the authors did not test the underlying process model empirically. The only study that empirically tested the SSMMD as applied to the dropout process was conducted by Connell et al. (1995) . The results of the present study not only confirm Connell el al.’s finding, but also extend it to a nationally representative sample of high school students. Additionally, by measuring engagement as a multidimensional rather than a unidimensional construct we underline the importance of behavioral and academic engagement in the dropout process.

Second, this study provides further support for the role of social context in self-system processes and school engagement. Most research to date has focused on the impact of teachers ( Patrick et al., 2007 ; Ryan & Patrick, 2001 ) or of parents ( Hong & Ho, 2005 ; You & Sharkey, 2009 ) on student self-system processes and school engagement. Very little work has compared the relative impact of the two sources of social support. Results from this study suggest when teachers show interest in students, praise their efforts, and contribute to community building within the school; they directly influence students’ perception of self and nurture students’ levels of school engagement. Similarly, when parents speak frequently with their children about school-related topics, they contribute to students’ sense of identification with school, their general perception of control. As control and identification with school are enhanced, these energizing internal mechanisms motivate students to be academically and behaviorally engaged in school activities.

Third, the findings suggest that students’ self-systems affect their school engagement and academic achievement. This result not only confirms previous findings ( Furrer & Skinner, 2003 ; Legault, Green-Demers, & Pelletier, 2006 ; Skinner et al., 2008 ), but also provides new evidence about the magnitude of the effects. The direct effect of identification with school on behavioral engagement ( β = .24) was twice the magnitude of the direct effect of identification with school on academic engagement ( β = .04). In addition, the effect of perceived control on academic achievement ( β = .39) was about twice the magnitude of the effect on academic engagement ( β = .19). Contrary to our expectation, the effect of perceived control on behavioral engagement was not significant ( β = .03). Hence, our findings suggest that behavioral engagement was more influenced by perceived identification with school, and academic engagement and achievement were more related to perceived control. That is, students who identify with their school are more likely to conform to classroom rules and regulations, and students who believe in their ability to control the outcome of their educational experience are much more likely to work hard, complete homework, be attentive in mathematics and English classes, and score higher on achievement tests.

Fourth, our results suggest that behavioral and academic engagement and academic achievement are key variables to consider when predicting high school dropout. This finding is in line with research that has shown that behavioral disengagement ( Archambault et al., 2009 ; Ekstrom, Goertz, Pollack, & Rock, 1986 ; Finn, 2006 ; Ream & Rumberger, 2008 ) and academic achievement ( Battin-Pearson et al., 2000 ; Hardre & Reeve, 2003 ) are precursors of dropping out of high school. However, the present findings expand this research and provide evidence that academic engagement also is also a significant predictors of dropping out of high school and that it’s utility in predicting dropout is similar to that of behavioral engagement. Educators and policymakers interested in preventing school dropout may want to consider how to implement intervention strategies aimed at increasing students’ academic and behavioral engagement and academic achievement ( Reschly, 2010 ).

Fifth, this study suggests that academic and behavioral engagement are critical mediators between self-system processes and dropping out of high school. This finding suggests that students’ perception of control and identification with school may serve a dynamic purpose by initiating and sustaining a willingness to participate in academic activities and to conform to school rules and regulations, which in turn decrease the likelihood of dropping out of high school.

The current study has several limitations. First, the data were from an extant database; therefore, the measures of parent support and teacher support were limited in scope and design. Parent support is a multidimensional construct ( Epstein, 1995 ; Fan, 2001 ). However, in this study, we examined only one dimension of parent support. More studies employing the multidimensional approach of parent involvement are warranted. Similarly, we focused on only one facet of school context: teacher support. The ELS did not collect data about further aspects of teacher work, including support of autonomy and promotion of performance goals. Future research should investigate these aspects of teachers’ work. Second, we relied mostly on self-report information from students and teachers to assess perception of social context, perception of self, and school engagement. Although self-report measures are appropriate “when the theory or construct involved is attitudinal or perceptual” ( Schmitt, 1994 , p. 393), one could draw a more comprehensive picture by implementing multiple methodologies (e.g., observations). Third, findings are based on two time point. Thus, it is not known how results might vary if studied across multiple time points. Future research with longitudinal data could address this limitation.

In summary, despite the limitations, the findings of the present study are significant for both theory and practice. The study contributes to the literature by explicating the contributions and interactions of social context, self-system processes, and school engagement in predicting dropping out from high school. More specifically the present results highlight the centrality of supportive teachers and parents for promoting positive self-perceptions of control and identification with school and for nurturing student academic and behavioral engagement. Our results also underscore the importance of behavioral and academic engagement and academic achievement in predicting dropping out of high school. Our data offer further evidence that behavioral and academic engagement mediate the link between self-systems and dropping out of high school and that self-perceptions mediate the relations between teacher and parent support and academic and behavioral engagement. Future studies that focus on applying SSMMD to high school dropouts might consider testing this model across genders and ethnic groups.

☆ This research was supported by two grants from theInstitute of Education Sciences, U.S. Department of Education (R3214A100022 and R305F100013). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Institute of Education Sciences or the U.S. Department of Education.

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Understanding Dropouts: Statistics, Strategies, and High-Stakes Testing (2001)

Chapter: 1. background and context, 1 background and context.

F ailure to complete high school has been recognized as a social problem in the United States for decades and, as discussed below, the individual and social costs of dropping out are considerable. Social scientists, policy makers, journalists, and the public have pondered questions about why students drop out, how many drop out, what happens to dropouts, and how young people might be kept from dropping out. Currently, many voices are arguing about the effects of standards-based reforms and graduation tests on students' decisions to drop out and about which dropout counts are correct. A significant body of research has examined questions about dropouts, and this section of the report provides an overview of current knowledge about these young people. We begin with a look at the history of school completion.

CHANGING EXPECTATIONS FOR STUDENTS

Expectations for the schooling of adolescents in the United States have changed markedly in the past 100 years. Indeed, the very notion of adolescence as a phase of life distinct from both childhood and adulthood came into common parlance only in the first decades of the twentieth century, at roughly the same time that educators began to develop increasingly ambitious goals for the schooling of students beyond the eighth grade ( Education Week, 2000:36). At the turn of the last century, as Sherman Dorn noted in the paper he prepared for the workshop, “fewer than one of every

ten adolescents graduated from high school. Today, roughly three of every four teens can expect to earn a diploma through a regular high school program” (Dorn, 2000:4).

High school in the early part of the century was a growing phenomenon, but it was still made available primarily to middle- and upper-class students and was generally focused on rigorous college preparatory work. At the turn of the century, the lack of a high school diploma did not necessarily deter young people from going on to successful careers in business or politics. As the number of students enrolled in high school grew, from approximately 500,000 in 1900 to 2.4 million in 1920 and then to 6.5 million in 1940, notions of the purpose of postelementary schooling were evolving.

Dorn provided the committee with an overview of trends in graduation rates over the twentieth century, noting three features of the overall trend that stand out: 1 (1) a steady increase in graduation rates throughout the first half of the twentieth century; (2) a decrease around the years during and immediately after the Second World War; (3) a plateau beginning with the cohort of students born during the 1950s. He discussed possible explanations for these changes in school completion rates.

One possible explanation is the influence of changes in the labor market. A number of developments had the effect of excluding increasing numbers of young people from full-time employment in the early decades of the twentieth century, including the mechanization of agriculture, increases in immigration, and the passage of new child labor laws. As teenagers had more difficulty finding work, increasing numbers of them stayed enrolled in school. The dip during the later 1940s is correspondingly explained by the fact that it was not only adult women who moved into the workforce to replace male workers who left employment for military service, but also teenagers of both sexes. The postwar dip and plateau also correlates with the growing availability of part-time employment and other labor opportunities for teenagers, which challenged the perception that completing school was important to financial success.

Dorn describes a pattern in which participation in successive levels of schooling gradually increases until the pressure spills over into the next level. Increasing proportions of the potential student population tend to

1 Dorn based his discussion of the trendlines on the Current Population Survey, census data, and state and district administrative data sources.

participate in schooling to a given level until saturation is reached—that is, until virtually all are enrolled. Expectations regarding participation in the next level then expand, and the pattern is repeated. In the United States, the norm has moved from primary schooling, to the eighth-grade level, and then to high school completion. State laws regarding school enrollment have moved along with these expectations. Currently, most states require that students stay enrolled through the age of 16. The steady increase in high school enrollment during the first half of the century thus reflects the gradual development of the now widely shared conviction that all teenagers should complete high school. Current political discourse reflects a developing expectation that the majority of students will not just complete high school but also participate in some form of higher education.

It was not until the 1960s that dropping out was widely considered a social problem because it was not until midcentury that sufficient percentages of young people were graduating from high school so that those who did not could be viewed as deviating from the norm. Dorn illustrated the views of dropping out that were becoming current in that period with this 1965 quotation from sociologist Lucius Cervantes (quoted in Dorn, 2000:19):

It is from this hard core of dropouts that a high proportion of the gangsters, hoodlums, drug-addicted, government-dependent prone, irresponsible and illegitimate parents of tomorrow will be predictably recruited.

A number of scholars have argued that as enrollments have increased, high schools' missions have evolved. Many jurisdictions responded to the arrival of waves of immigrants by making it more difficult for families to avoid enrolling their children in school, arguing that public schools were the best vehicle for assimilating these new citizens and would-be citizens ( Education Week, 2000:4). As the children of the lower and middle classes entered high school, however, expectations and graduation standards were lowered. Thus, the postwar plateau might also be explained by the notion that, as Dorn put it, “by the 1960s high schools really had succeeded at becoming the prime custodians for adolescents” (Dorn, 2000:10). If high schools were actually providing little benefit for the students on the lower rungs of the socioeconomic ladder, according to this reasoning, there was little motivation for increasing the graduation rate from 70 or 80 percent to 100 percent.

Another notable trend was the general decrease in gaps between completion rates for whites and nonwhites and other population subgroups.

Observers have noted that this narrowing of the gap relates to the saturation effect described earlier—completion rates for Hispanics and African Americans have moved up while those for whites have remained level (Cameron and Heckman, 1993a:5). At the same time, however, alternative notions of school completion have proliferated (discussed in greater detail below). Dorn called attention to the fact that in Florida six different types of diplomas are available and that other states have adopted similar means of marking differing levels of achievement. The categories of school completion are not fixed and apparently not of equivalent value; it may be that many minority students who have converted statistically from dropouts to school completers have in fact moved to an in-between status that needs to be better understood. This circumstance significantly complicates the task of statisticians and others who attempt to keep track of students' progress through school. It also complicates policy discussions about social goals for young people, expectations of the education system, and possible solutions to the problem of dropouts.

LOOKING AT DROPOUTS

A recent report from the National Center for Education Statistics (NCES) shows that five percent of all young adults who were enrolled in grades 10-12 (519,000 of 10,464,000) dropped out of school between October 1998 and October 1999 (National Center for Education Statistics, 2000:iii). That report provides a wealth of other important information, noting, for example, that Hispanic and African American students are significantly more likely than white students to drop out and that students from poor families are far more likely to drop out than are students from nonpoor families. The report provides information on trends in dropout rates over time and comparisons among students by age, racial and ethnic characteristics, and the like.

The statistical information in this and other reports is valuable, but it provides only a snapshot of the situation across the country. General statistical reports are not designed to reveal the effects of particular policies, programs, and educational approaches on particular groups of students, but variations in the numbers suggest possible sources of more detailed understanding. School completion rates reported by states and districts show wide variation, for example, from 74.5 percent for Nevada to 92.9 percent for Maine. The rates at which students complete school vary over time and are different for different population subgroups, regions, and kinds

of schools, and for students who differ in other ways. (The school completion rate is only one of several ways of measuring dropout behavior; see discussion below). The reported data (from NCES) suggest that particular factors are associated with dropping out, such as single-parent homes, teenage pregnancy, history of academic difficulty, and retention in grade. Other researchers have identified specific school factors that are associated with dropping out, discussed below.

The rates can be calculated in different ways, which means that dropout or school completion rates for the same jurisdiction can look very different, depending on which method is used. Indeed, there is no single dropout measure that can be relied on for analysis; there are many rates based on different definitions and measures, collected by different agents for different purposes. The NCES report, for example, opens by presenting two calculations of dropouts, 5 percent and 11 percent, respectively, for slightly different groups, as well as a percentage of school completers, 85.9 percent (2000:iii).

The confusion about counting dropouts is not surprising when one considers the challenges of counting students in different categories. Numerous decisions can drastically affect the count: At what point in the school year should student enrollment be counted? Should it be done at every grade? How long should a student's absence from school be to count as dropping out? What age ranges should be considered? What about private and charter schools and students who are home-schooled? In most school districts and states, significant numbers of students move into and out of their jurisdictions each year, so school careers are difficult to track. Even within a jurisdiction, many students follow irregular pathways that are also difficult to track—they may drop out of school temporarily, perhaps more than once, before either completing or leaving for good. Different jurisdictions face different statistical challenges, depending on the composition of their student populations. Districts with high immigrant populations may have large numbers of young people who arrive with little documentation of their previous schooling, so that determining which among them have completed school is difficult. What students do after dropping out is also highly variable. Alternative educational and vocational programs, which may or may not be accredited means of completing secondary schooling requirements, have proliferated. A significant number of students take the General Educational Development (GED) Test every year; many (but not all) of them receive school completion credentials from their states.

Tracking dropout behavior is clearly messy. In response, statisticians have devised a variety of ways of measuring the behavior: status dropout rates, event dropout rates, school completion rates, and more. Unfortunately, the many measures often lead to confusion or misunderstanding among people trying to use or understand the data. A later section of this report addresses in greater detail some of the reasons why measuring this aspect of student behavior is complicated and describe what is meant by some of the different measures that are available. First, however, it is worth summarizing the general picture of high school dropouts that has emerged from accumulated research. These general observations describe trends that are evident regardless of the method by which dropouts are counted.

WHO DROPS OUT

The overall rate at which students drop out of school has declined gradually in recent decades, but is currently stable. A number of student characteristics have been consistently correlated with dropping out over the past few decades. 2 First and most important, dropping out is significantly more prevalent among Hispanic and African American students, among students in poverty, among students in urban schools, among English-language learners, and among students with disabilities than among those who do not have these characteristics. The characteristics of the students most likely to drop out illustrate one of the keys to understanding the phenomenon: that dropping out is a process that may begin in the early years of elementary school, not an isolated event that occurs during the last few years of high school. The process has been described as one of gradual disengagement from school. The particular stages and influences vary widely, but the discernible pattern is an interaction among characteristics of the family and home environment and characteristics of a student's experience in school.

Family and Home Characteristics

Income In general, students at low income levels are more likely to drop out of school than are those at higher levels. NCES reports that in

2 Data in this section are taken from National Center for Education Statistics (1996, 2000), which are based on the Current Population Survey. The numbers are event dropout rates.

1999 the dropout rate for students whose families were in the lowest 20 percent of income distribution was 11 percent; for students whose families fall in the middle 60 percent it was 5 percent; and for students from families in the top 20 percent it was 2 percent.

Race/Ethnicity Both Hispanic and African American students are more likely to drop out than are white students, with the rate for Hispanic students being consistently the highest. In 1999, 28.6 percent of Hispanic students dropped out of school, compared with 12.6 percent of black students and 7.3 percent of white students. It is important to note that among Hispanic youths, the dropout rate is significantly higher for those who were not born in the United States (44.2%) than for those who were (16.1%). Two important issues relate to this last point: first, a significant number of foreign-born Hispanic young people have never been enrolled in a U.S. school. Second, the majority of those who were never enrolled have been reported as speaking English “not well” or “not at all.” The status of Hispanic young people offers an illustration of the complexities of counting dropouts. Young people who have never been enrolled in a U.S. school but have no diploma typically show up in measures of status dropout rates (people of a certain age who have no diploma) but not in measures of event dropout rates (students enrolled in one grade but not the next who have not received a diploma or been otherwise accounted for). This issue is addressed in greater detail below.

Family Structure Research has shown an increased risk of academic difficulty or dropping out for students who live in single-parent families, those from large families, and those, especially girls, who have become parents themselves. Other factors have been noted as well, such as having parents who have completed fewer years of schooling or who report providing little support for their children's education, such as providing a specific place to study and reading materials.

School-Related Characteristics

History of Poor Academic Performance Not surprisingly, poor grades and test scores are associated with an increased likeliness to drop out, as is enrollment in remedial courses.

Educational Engagement Researchers have used several measures of stu-

dents' educational engagement, including hours of television watched, hours spent on homework, hours spent at paid employment, and frequency of attending class without books and other necessary materials. Each of these factors has been associated with increased likeliness to encounter academic difficulties and to drop out. That is, the more time a student spends at a job or watching television, the more likely he or she is to drop out. Students who spend relatively little time on homework and who are more likely to attend school unprepared are similarly at increased risk of dropping out.

Academic Delay Students who are older than the normal range for the grade in which they are enrolled are significantly more likely to drop out of school than are those who are not. Similarly, students who have received fewer than the required number of academic credits for their grade are more likely to drop out than other students are.

Interactions

Risk factors tend to cluster together and to have cumulative effects. The children of families in poverty, for example, have a greater risk of academic difficulty than do other children, and they are also at greater risk for poor health, early and unwanted pregnancies, and criminal behavior, each of which is associated with an increased risk of dropping out (National Center for Education Statistics, 1996:11). Urban schools and districts consistently report the highest dropout rates; the annual rate for all urban districts currently averages 10 percent, and in many urban districts it is much higher (Balfanz and Legters, 2001:22). Student populations in these districts are affected by the risk factors associated with dropping out, particularly poverty, in greater numbers than are students in other districts.

WHY STUDENTS DROP OUT

Students who have dropped out of school have given three common reasons ( ERIC Digest, 1987:1):

• A dislike of school and a view that school is boring and not relevant to their needs;
• Low academic achievement, poor grades, or academic failure; and
• A need for money and a desire to work full-time.

These responses in no way contradict the statistical portrait of students who drop out in the United States, but they offer a somewhat different perspective from which to consider the many factors that influence students' decisions about school and work. Shifts in the labor market can have profound effects on students' behavior that are evident in national statistics, particularly those that track changes over many years. Scholars have also identified socioeconomic factors that correlate with the likelihood of a student's dropping out. However, each student whose life is captured in dropout statistics is an individual reacting to a unique set of circumstances. The circumstances that cause a particular student to separate from school before completing the requirements for a diploma can rarely be summed up easily, and rarely involve only one factor. Nevertheless, educators and policy makers alike see that dropping out of school diminishes young people's life chances in significant ways, and look for ways to understand both why they do it and how they might be prevented from doing it.

Dropping Out as a Process

Rumberger summarizes a key message from the research on the factors associated with dropping out:

Although dropping out is generally considered a status or educational outcome that can readily be measured at a particular point in time, it is more appropriately viewed as a process of disengagement that occurs over time. And warning signs for students at risk of dropping out often appear in elementary school, providing ample time to intervene (Rumberger, 2000:25).

Beginning with some points that can be difficult to discern in the complex statistics about dropping out, Rumberger noted that the percentage of young people who complete high school through an alternative to the traditional course requirements and diploma (through the GED or a vocational or other alternative) has grown: 4 percent used an alternative means in 1988 while 10 percent did so in 1998—though the calculated school completion rate among 18- to 24-year-olds remained constant at about 85 percent (Rumberger, 2000:7). Several longitudinal studies show that a much larger percentage of students than are captured in event or status dropout calculations drop out of school temporarily for one or more periods during high school. Doing so is associated with later dropping out for good, with a decreased likelihood of enrolling in postsecondary schooling, and with an increased likelihood of unemployment.

Focusing on the process that leads to the ultimate decision to drop out, Rumberger stresses the importance of interaction among a variety of contributing factors: “if many factors contribute to this phenomenon over a long period of time, it is virtually impossible to demonstrate a causal connection between any single factor and the decision to quit school” (Rumberger, 2001:4). Instead, researchers have looked for ways to organize the factors that seem to be predictive of dropping out in ways that can be useful in efforts to intervene and prevent that outcome. As noted above, two basic categories are characteristics of students, their families and their home circumstances, and characteristics of their schooling.

Rumberger pays particular attention to the concept of engagement with school. Absenteeism and discipline problems are strong predictors of dropping out, even for students not experiencing academic difficulties. More subtle indicators of disengagement from school, such as moving from school to school, negative attitude toward school, and minor discipline problems can show up as early as elementary and middle school as predictors of a subsequent decision to drop out. The role of retention in grade is very important in this context:

. . . students who were retained in grades 1 to 8 were four times more likely to drop out between grades 8 and 10 than students who were not retained, even after controlling for socioeconomic status, 8 th grade school performance, and a host of background and school factors (Rumberger, 2000:15).

Rumberger's work confirms other research on family characteristics that are associated with dropping out, particularly the finding that belonging to families lower in socioeconomic status and those headed by a single parent are both risk factors for students. He also looked at research on the role that less concrete factors may play. Stronger relationships between parents and children seem to reduce the risk of dropping out, as does being the child of parents who “monitor and regulate [the child's] activities, provide emotional support, encourage decision-making . . . and are generally more involved in [the child's] schooling” (Rumberger, 2000:17).

At the workshop, David Grissmer touched on some other factors that don't make their way into national statistics but that could play a significant role for many young people. He pointed to studies of hyperactivity and attention-deficit disorder that indicate that while the percentage of all young people affected is small, roughly 5 percent, the percentage of high school dropouts affected is much larger—perhaps as much as 40 percent. He noted that dyslexia, depression, and other cognitive or mental health

problems can have significant effects on students' capacity to learn and flourish in the school environment, but that these situations are often overlooked in statistical analyses.

Schools also play a role in outcomes for students. Rumberger presented data showing that when results are controlled for students' background characteristics, dropout rates for schools still vary widely. Rumberger's (2000) review of the literature on school effects identifies several key findings:

• The social composition of the student body seems to influence student achievement—and affect the dropout rate. That is, students who attend schools with high concentrations of students with characteristics that increase their likelihood of dropping out, but who don't have those characteristics themselves, are nevertheless more likely to drop out. This finding relates to the fact that dropout rates are consistently significantly higher for urban schools and districts than for others (Balfanz and Legters, 2001:1).
• Some studies suggest that school resources can influence the dropout rate through the student-teacher ratio and possibly through teacher quality.
• The climate, policies, and practices of a school may have effects on dropping out. Indicators of the school climate, such as attendance rates and numbers of students enrolled in advanced courses, may be predictive of dropping out. There is some evidence that other factors, such as school size, structure, and governance, may also have effects.

Interventions

A variety of different kinds of evidence point to the importance of early attention to the problems that are associated with subsequent dropping out. The correspondence between the many risk factors that have been enumerated is not, however, either linear or foolproof. Dynarski (2000) notes that despite strong associations between a variety of characteristics and dropping out, using individual risk factors as predictors is tricky: research that has evaluated the predictive value of risk factors has shown that the one “that was best able to predict whether middle school students were dropouts—high absenteeism—correctly identified dropouts only 16 percent of the time” (Dynarski, 2000:9).

A quantitative look at the effectiveness of dropout prevention pro-

grams can seem sobering, but it is important to bear in mind that even a perfectly successful program—one that kept every potential dropout in school—would affect only a small fraction of students. Any program that is an attempt to intervene in time to prevent dropping out must begin with a group of students who share defined risk factors, but of whom only a fraction would actually have dropped out. That is, even among groups of students with many risk factors, the dropout rate rarely goes over approximately 15 percent, and it is only these 15 of 100 students who receive an intervention whose fates could potentially be changed. When resources are limited, correctly identifying the students who will benefit most from intervention (those who are most likely to drop out) is clearly important. However, since many different kinds of factors affect dropout behavior, using them as predictors is not easy. This point is also relevant to Rumberger's point that if numerous factors contribute to a multiyear process of dropping out, isolating a cause or an effective predictor would logically be very difficult.

Though the quantitative evidence of effectiveness is not overwhelming, Dynarski (2000) used the results of a Department of Education study of the effectiveness of dropout prevention programs to provide a description of some of the strategies that seem to work best. Providing individual-level counseling to students emerged as a key tool for changing students' thinking about their education. Another tool was creating smaller school settings, even within a large school, if necessary. Students are more likely to become alienated and disengaged from school in larger settings, and are likely to receive less individualized attention from teachers and staff. 3 Not surprisingly, providing counseling and creating smaller school settings requires more staff, and, in turn, the expenditure of more resources per pupil (Dynarski, 2000).

Others who have explored the effectiveness of dropout prevention programs have come to conclusions that amplify and support Dynarski's findings. McPartland and Jordan (2001) advocate, among other things, that high schools be restructured to provide smaller school settings and to both increase student engagement with school and strengthen students' relationships with school staff. McPartland has also suggested specific supports for students who enter high school unprepared for challenging academic work,

3 The work of Lee and Burkam (2001), Fine (1987), and others on the structure of high schools is relevant to this point.

including extra time to complete courses and remediation outside of school hours.

In summary, the committee finds several important messages in the research on dropout behavior:

• A number of school-related factors, such as high concentrations of low-achieving students, and less-qualified teachers, for example, are associated with higher dropout rates. Other factors, such as small school settings and individualized attention, are associated with lower dropout rates.
• Many aspects of home life and socioeconomic status are associated with dropout behavior.
• Typically, contributing factors interact in a gradual process of disengagement from school over many years.

Conclusion: The committee concludes that identifying students with risk factors early in their careers (preschool through elementary school) and providing them with ongoing support, remediation, and counseling are likely to be the most promising means of encouraging them to stay in school. Using individual risk factors to identify likely dropouts with whom to intervene, particularly among students at the ninth-grade level and beyond, is difficult. Evidence about interventions done at this stage suggests that their effectiveness is limited.

The role played by testing in the nation's public school system has been increasing steadily—and growing more complicated—for more than 20 years. The Committee on Educational Excellence and Testing Equity (CEETE) was formed to monitor the effects of education reform, particularly testing, on students at risk for academic failure because of poverty, lack of proficiency in English, disability, or membership in population subgroups that have been educationally disadvantaged. The committee recognizes the important potential benefits of standards-based reforms and of test results in revealing the impact of reform efforts on these students. The committee also recognizes the valuable role graduation tests can potentially play in making requirements concrete, in increasing the value of a diploma, and in motivating students and educators alike to work to higher standards. At the same time, educational testing is a complicated endeavor, that reality can fall far short of the model, and that testing cannot by itself provide the desired benefits. If testing is improperly used, it can have negative effects, such as encouraging school leaving, that can hit disadvantaged students hardest. The committee was concerned that the recent proliferation of high school exit examinations could have the unintended effect of increasing dropout rates among students whose rates are already far higher than the average, and has taken a close look at what is known about influences on dropout behavior and at the available data on dropouts and school completion.

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Why Students Drop Out

Even though school completion rates have continually grown during much of past 100 years, dropping out of school persists as a problem that interferes with educational system efficiency and the most straightforward and satisfying route to individual educational goals for young people. Doll, Eslami, and Walters (2013) present data from seven nationally representative studies (spanning more than 50 years) regarding reasons students drop out of high school. Some excerpts are presented below in tables; however, for a complete discussion, please see the original article: “ Understanding Why Students Drop Out of High School, According to Their Own Reports ”

The selected tables are presented in opposite order than they appear in the article so as to present the most recent data first. Note also that survey questions varied from study to study (database to database) so caution should be taken in making comparisons across years and studies.

Included in the tables presented is an analysis of whether the reasons presented are considered “push,” “pull,” or “falling out” factors. The following briefly presents an explanation from Doll et al. (2013).

Jordan et al. (1994) explained pressures on students of push and pull dropout factors. A student is pushed out when adverse situations within the school environment lead to consequences, ultimately resulting in dropout. . . . [S]tudents can be pulled out when factors inside the student divert them from completing school. . . . Watt and Roessingh (1994) added a third factor called falling out of school, which occurs when a student does not show significant academic progress in schoolwork and becomes apathetic or even disillusioned with school completion. It is not necessarily an active decision, but rather a “side-effect of insufficient personal and educational support” (p. 293).

The National Dropout Prevention Center (NDPC) exists to support those who work to improve student success and graduation rates. NDPC offers a wide range of resources and services to schools, districts, regional agencies, and states. Contact NDPC by (email:  [email protected]  or phone: (864-642-6372.).

Featured Resources

• DOI: 10.3102/00346543057002101
• Corpus ID: 145621478

High School Dropouts: A Review of Issues and Evidence

• R. Rumberger
• Published 1 June 1987
• Review of Educational Research

Tables from this paper

1,139 Citations

The economics of high school dropouts, why students drop out of school: a review of 25 years of research, the high school dropout: antecedents and alternatives, rural public school dropouts: findings from high school and beyond., school dropout trends for elementary and secondary education in pune district (an exploratory study based on u–dise data), analyzing high school dropouts as a social problem: policy considerations, non-school correlates of dropout: an integrative review of the literature, students at risk for school dropout: supporting their persistence, missing measures of the who and why of school dropouts: implications for policy and research., reducing hispanic dropout: a case of success, 62 references, large school systems' dropout reports: an analysis of definitions, procedures, and findings, dropping out: how much do schools contribute to the problem, raising standards and retaining students: the impact of the reform recommendations on potential dropouts, why urban adolescents drop into and out of public high school, a population at risk: potential consequences of tougher school standards for student dropouts, who drops out of high school and why findings from a national study, dropping out of high school: the influence of race, sex, and family background, race, class, and gender in education research: an argument for integrative analysis, taking stock: renewing our research agenda on the causes and consequences of dropping out, the effects of alternative school programs on high school completion and labor market outcomes, related papers.

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High School Dropout Questionnaire & Sample Survey Template

This free high school dropout questionnaire can be used in your surveys to help collect demographic information, as well as understand the reasons for high school student dropouts. The survey questions in this sample survey template are designed to draw conclusions about the dropout rate and the reasons that cause it. You can use this survey to monitor trends of students giving up high school and mitigate the reasons that cause them. Use this dropout prevention survey template to collect in-depth data and identify the factors that will help keep students in school.

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Helping High School Dropouts Improve Their Prospects

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Dan bloom and db dan bloom director, health and barriers to employment policy area, mdrc ron haskins ron haskins senior fellow emeritus - economic studies.

April 27, 2010

Dropping out of high school has serious long-term consequences not only for individuals but also for society. According to expert estimates, between 3.5 million and 6 million young Americans between the ages of 16 and 24 are school dropouts. Lowering the number of adolescents who fail to finish high school and helping those who drop out get back on track must be a major policy goal for our nation. In this policy brief we focus primarily on how best to provide youngsters who have dropped out of school a second chance, though we also give some attention to dropout prevention (we do not tackle the topic of high school reform more broadly). Several carefully evaluated program models hold out promise that they can help both young people at risk of dropping out and those who do drop out. These promising programs must be expanded and continually improved, and we offer specific proposals for doing so. U.S. policy must aim to keep as many young Americans as possible in high school until they graduate and to reconnect as many as possible of those who drop out despite educators’ best efforts to keep them in school.

Just how costly is school dropout? Americans who do not graduate from high school pay a heavy price personally. Although correlation is not causation, the links between leaving school before graduating and having poor life outcomes are striking. Perhaps the most important correlation is that between dropping out and low income. Based on Census Bureau data (from 1965 to 2005), figure 1 compares the median family income of adults who dropped out of high school with that of adults who completed various levels of education. Two points are notable. First, in 2005, school dropouts earned $15,700 less than adults with a high school degree and well over $35,000 less than those with a two-year degree. Over a forty-five-year career the earnings difference between a dropout and someone with only a high school degree can amount to more than $700,000. Considered from a broader social perspective, the income-education pattern illustrated by figure 1 shows that school dropouts contribute substantially to the problem of income inequality that is now a growing concern of researchers and policy makers.

Dropping out of school is also linked with many other negative outcomes such as increased chances of unemployment or completely dropping out of the workforce, lower rates of marriage, increased incidence of divorce and births outside marriage, increased involvement with the welfare and legal systems, and even poor health. All these outcomes are costly not only to dropouts personally, but also to society. Prison costs, for example, are among the most rapidly growing items in nearly every state budget, and more than two-thirds of state prison inmates are school dropouts, though many obtain a General Educational Development (GED) credential while in prison. Similarly, in 2006, 67 percent of all births to young dropouts were outside marriage, compared with 10 percent of births for women with a master’s degree. Because families with children born outside marriage are five or six times more likely to live in poverty than married-couple families, it follows that they are also more likely to be on welfare. In both these examples, dropping out is linked with social problems that impose large public costs on the nation.

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High school dropouts: A review of issues and evidence

1987, Review of educational research

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Aaron Pallas

Steven Meeker , Stacey Edmonson

This study attempts to answer the following research question: What are the factors that prevent students from completing high school? The participants in this study included 228 current and recent students from General Educational Development (GED) programs across the state of Texas. In an effort to gain a clearer picture of circumstances faced by current high school students, only responses from participants in their teens or twenties were considered for the research concerning dropout factors. There were 158 respondents in this category. Data for this qualitative study were collected by means of surveys containing open-ended questions, focus groups, and semi-structured interviews. The significant findings of this study are as follows: (1) More than a quarter of the 158 participants in this study reported that pregnancy and parenting prevented them from graduating high school; (2) More than one-sixth reported that conflicts with school personnel as well as overall school dysfuncti...

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Where and when does progress begin with our high school dropout problem? Is it simply bringing new technology to the class room as Morrell (2009) and Mahiri (2011) suggest? Is it tracking and detecting the potential dropout, and implementing intervention strategies as Heppen & Bowles (2008) suggest? If the ERO (20100 study was correct, and if 0.09% improvement is what can be hoped through Enhanced Reading Opportunities, what can be done with greater improvement? The Governor’s Report (2010) suggest that high school dropout behavior is predictable, why isn’t an intervention program in place now? Berliner (2008) saw an increase in high school graduation from those who reenrolled, so is that the strategy? Push to reenroll? Princotta & Harris (2009) suggest extended hours and extended days. Will that work, and if so, why is it not in place now? Hammond (2009) looks at teacher training institutions as the solution, yet it leans on Apple’s (2002; 2009; 2010) contention that the social structure of a hegemonic society is the obstacle to open discussions. Deli-Amen (2011) and Durkheim (1951) emphasize academic integration and social integration as effective models at addressing the achievement gap, and so does Rumberger (2011). As salient as it may be, the fissure of perceived inequality based on racial differences, will not be resolved through hierarchical mediation. That strategy reinforces the Hammond model of employing teaching graduates from the privileged institutions, and thereby purveyors of the pedagogical ideology of those institutions, which may not reflect the student population, like that of Paulo Freire’s conscientização. If the objective of learning is to develop higher level critical thinking skills (Dewey), then focusing on achieving an in-depth understanding of the world, allowing for the perception and exposure of perceived social and political contradictions, including the inequalities in the social stratification of ethnicities in our schools, will naturally lead to a form of critical consciousness that necessitates taking action against the oppressive elements in one's life that are illuminated by that understanding. The solution may rest in the uncharted territory of historical consciousness of the collective achievement gap carved out of our humanity, with a bit of 21st century technology to articulate the lighted path in the abyss between those two eternities of darkness.

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